Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for performing flow cytometry, comprising: a laser for generating laser radiation for illuminating a sample in a flow stream, at least one detector arranged to detect at least a portion of a radiation emanating from a particle in the sample in response to said illumination, an analysis module operably connected to the detector, the analysis module comprising a processor having memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor cause the processor to: receive a temporal signal from the at least one detector; and perform a statistical analysis of said temporal signal based on a forward model to reconstruct an image of the particle in said sample in the flow stream.
2. The system of claim 1 , wherein said laser radiation includes at least two beams of light having optical frequencies shifted from one another by a radiofrequency and said temporal signal comprises a time-frequency waveform comprising a beat frequency corresponding to a difference between said at least two optical frequencies.
3. The system of claim 2 , wherein said at least two beams of light comprises a beam of light having a local oscillator frequency and a plurality of comb beams that each have a frequency that is shifted from the local oscillator frequency by a radiofrequency.
4. The system of claim 2 , wherein said radiofrequency is in a range of about 10 MHz to about 250 MHz.
5. The system of claim 1 , wherein said radiation emanating from the sample is fluorescent radiation and said temporal signal is a fluorescent signal on which the analysis module operates to form a fluorescence image of the sample.
6. The system of claim 1 , wherein said radiation emanating from the sample is scattered laser radiation and said analysis module operates on said temporal signal to generate a darkfield image of the sample.
This invention relates to a system for analyzing samples using scattered laser radiation to generate a darkfield image. The system addresses the challenge of detecting and imaging small or weakly scattering particles, which are often difficult to observe using conventional brightfield microscopy. By leveraging laser illumination and darkfield imaging techniques, the system enhances contrast and sensitivity, enabling the detection of sub-micron particles or subtle structural features that would otherwise be invisible. The system includes a laser source that directs coherent radiation onto a sample. The radiation interacts with the sample, and the scattered laser radiation is collected by a detection module. This module captures the temporal signal of the scattered radiation, which contains information about the sample's structure and composition. An analysis module processes this temporal signal to generate a darkfield image, where the scattered light is isolated from the direct illumination, improving visibility of fine details. The system may also incorporate additional components, such as optical elements for focusing or filtering the laser radiation, or computational algorithms for enhancing image resolution or extracting quantitative data from the darkfield image. The use of laser radiation ensures high-intensity, monochromatic illumination, while the darkfield imaging technique suppresses background noise, making it particularly useful in applications like particle analysis, biological imaging, or material characterization.
7. The system of claim 1 , wherein said radiation emanating from the sample is a portion of said laser radiation that is transmitted through the sample and said analysis module operates on said temporal signal to form a brightfield image of the sample.
8. The system of claim 1 , wherein said statistical analysis is based on generating maximum likelihood estimates of one or more parameters of said model.
This invention relates to a system for analyzing data using statistical models, particularly for estimating parameters in a probabilistic framework. The system addresses the challenge of accurately modeling complex datasets by employing maximum likelihood estimation (MLE) techniques to derive parameter values that best fit observed data. The core system includes a data processing module that receives input data and a model definition specifying the probabilistic relationships between variables. A statistical analysis module then applies MLE to compute parameter estimates that maximize the likelihood function, given the observed data and model structure. This approach improves parameter estimation accuracy compared to traditional methods, especially in scenarios with noisy or incomplete data. The system may also incorporate additional features such as iterative refinement of estimates, handling of missing data, and integration with machine learning algorithms to enhance model performance. By leveraging MLE, the system provides robust parameter estimates that can be used for prediction, inference, or decision-making in various applications, including finance, healthcare, and engineering. The invention focuses on automating the parameter estimation process while ensuring statistical rigor and computational efficiency.
9. The system of claim 1 , wherein said statistical analysis employs any of least squares optimization, gradient descent optimization, particle swarm optimization, genetic algorithm optimization, parametric estimation and Bayesian spectral estimation.
10. The system of claim 1 , wherein said analysis memory comprises instructions stored thereon, which when executed by the processor cause the processor to form an initial image of the sample and employ said image as a seed image for said statistical analysis to generate said reconstructed image with an improved signal-to-noise ratio or an improved resolution relative to the seed image.
11. The system of claim 10 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to employ Fourier transformation or lock-in detection for forming said initial image.
12. The system of claim 1 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to generate a model of the detected temporal signal with said forward model as a plurality of temporal segments.
13. The system of claim 12 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to perform statistical analysis that employs a least squares regression analysis so as to obtain values for parameters associated with said modeled temporal segments by minimizing a sum of squared residuals corresponding to differences between said modeled and respective measured temporal segments.
14. The system of claim 13 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to model each of said temporal segments to include one sinusoidal and one cosinusoidal term.
15. The system of claim 1 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to employ a least squares analysis to obtain one or more estimates of one or more parameters associated with a model of an image of the illuminated sample by minimizing a sum of squared residuals corresponding to a difference between said detected temporal signal and a temporal signal inferred from said forward model.
16. The system of claim 1 , wherein said forward model comprises a non-linear model.
17. The system of claim 16 , wherein said statistical analysis comprises a gradient descent optimization method.
18. The system of claim 17 , wherein said gradient descent optimization method calculates an error gradient indicative of a distance between an expected temporal signal based on said forward model and the measured temporal signal and iteratively calculates an updated image of the sample by stepping a previous image down the error gradient.
19. The system of claim 18 , wherein said gradient descent optimization method starts said iterative calculation with an initial estimated image computed based on the measured temporal signal.
20. The system of claim 18 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to calculate said initial image via application of any of a Fourier transformation and lock-in detection to said measured temporal signal.
21. The system of claim 1 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to perform statistical analysis that employs a priori information about said measured temporal signal in combination with Bayesian spectral estimation to reconstruct said image of the sample.
22. The system of claim 21 , wherein said a priori information indicates that said temporal signal is composed of a plurality of sinusoids of unknown frequencies and amplitudes.
The system is designed for analyzing temporal signals, particularly those composed of multiple sinusoidal components with unknown frequencies and amplitudes. The system leverages a priori information to identify and characterize these sinusoidal components within the signal. This approach is useful in applications where signals contain overlapping or closely spaced sinusoidal waves, such as in communications, radar, or audio processing, where distinguishing individual frequency components is critical. The system processes the temporal signal to extract frequency and amplitude information, even when the exact parameters of the sinusoids are initially unknown. By utilizing prior knowledge about the signal structure, the system improves accuracy and efficiency in signal decomposition and analysis. This method is particularly valuable in scenarios where traditional Fourier-based techniques may struggle due to spectral leakage or limited resolution. The system may include preprocessing steps to enhance signal quality and post-processing to refine the extracted sinusoidal parameters. The overall goal is to provide a robust and reliable way to decompose complex temporal signals into their constituent sinusoidal components for further analysis or application.
23. The system of claim 22 , wherein said Bayesian spectral estimation provides an estimate of said unknown frequencies and amplitudes of said sinusoids.
A system for analyzing signals containing sinusoidal components addresses the challenge of accurately estimating unknown frequencies and amplitudes in noisy or complex environments. The system employs Bayesian spectral estimation techniques to process input signals, which may include multiple sinusoids embedded in noise or other interference. Bayesian spectral estimation is used to model the probabilistic relationships between observed signal data and the underlying sinusoidal parameters, providing robust estimates of frequency and amplitude values even in low signal-to-noise conditions. The system may incorporate prior knowledge or constraints to improve estimation accuracy, such as expected frequency ranges or amplitude bounds. Additionally, the system may include preprocessing steps to filter or condition the input signal before spectral estimation, enhancing the reliability of the Bayesian analysis. The output of the system provides a set of estimated frequencies and amplitudes, which can be used for further signal processing, monitoring, or control applications. This approach is particularly useful in fields such as communications, radar, sonar, and biomedical signal processing, where precise frequency and amplitude estimation is critical.
24. The system of claim 1 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to perform statistical analysis that employs a particle swarm optimization method.
25. The system of claim 1 , wherein said memory comprises instructions stored thereon, which when executed by the processor cause the processor to perform statistical analysis that employs a genetic algorithm.
26. The system of claim 1 , wherein said sample comprises any of a cell, a micro-vesicle, a cellular fragment, a liposome, a bead, and a small organism.
27. The system of claim 1 , wherein said laser radiation has a frequency is a range of about 300 THz to about 1000 THz.
This invention relates to a laser-based system designed for precise material processing, particularly in applications requiring high-frequency laser radiation. The system addresses the need for controlled and efficient material modification, such as cutting, drilling, or ablation, by utilizing laser radiation within a specific frequency range. The laser radiation operates at frequencies between approximately 300 THz and 1000 THz, corresponding to wavelengths in the near-infrared to visible spectrum. This frequency range is selected to optimize energy absorption in target materials, enabling fine control over processing parameters like depth, precision, and thermal effects. The system may include a laser source, beam delivery optics, and a control mechanism to regulate output parameters. The frequency range ensures compatibility with a variety of materials, including metals, semiconductors, and dielectrics, while minimizing collateral damage. The invention may also incorporate feedback mechanisms to adjust laser parameters dynamically, enhancing processing accuracy. The system is particularly useful in industries such as microfabrication, medical device manufacturing, and semiconductor production, where high-precision material processing is critical. The specified frequency range ensures efficient energy delivery while maintaining the necessary precision for advanced applications.
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March 2, 2021
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